Biological Insights of Multi-Omics Technologies in Human Diseases.

Format:

Book

Author/Creator:

Ali, Aarif.

Contributor:

U Rehman, Muneeb.

Ahmad, Sheikh Bilal.

Arafah, Azher.

Language:

English

Subjects (All):

Genomics.

Precision medicine.

Physical Description:

1 online resource (420 pages)

Edition:

1st ed.

Place of Publication:

San Diego : Elsevier Science & Technology, 2024.

Summary:

This book explores the integration of multi-omics technologies in understanding human diseases, providing a comprehensive overview of how these advanced methods can be applied in clinical and research settings. Edited by Aarif Ali and colleagues, the work covers various omics approaches, such as genomics, proteomics, metabolomics, and epigenomics, and their application in studying diseases like cancer, cardiovascular conditions, neurodegenerative disorders, and infectious diseases. The authors aim to highlight the advantages, challenges, and future prospects of utilizing multi-omics for biomarker discovery, therapeutic development, and disease management. Intended for researchers, clinicians, and professionals in the biomedical sciences, this book serves as a valuable resource for those seeking to leverage multi-omics in precision medicine. Generated by AI.

Contents:

Front Cover

Biological Insights of Multi-Omics Technologies in Human Diseases

Copyright

Contents

List of contributors

1 - Multiomics approaches in human diseases

1.1 Introduction

1.2 Multiomics strategies

1.2.1 Genomics

1.2.2 Epigenomics

1.2.3 Transcriptomics

1.2.4 Proteomics

1.2.5 Metabolomics

1.2.6 Lipidomics

1.3 Ethics

1.4 Historical perspective

1.5 Advantages and disadvantages of omics technologies

1.6 Future prospects

1.7 Conclusion

Glossary

Questions

Acknowledgments

References

Further readings

2 - Crosstalk of multiomics approaches with medicinal plants of therapeutic importance

2.1 Introduction

2.2 Synthesis and role of plant secondary metabolites

2.2.1 Terpenes

2.2.2 Monoterpenes

2.2.3 Sesquiterpenes

2.2.4 Diterpenes

2.2.5 Triterpenes

2.2.6 Phenolic compounds

2.2.7 Flavonoids

2.2.8 Nitrogen-containing compounds

2.2.9 Alkaloids

2.2.10 Cyanogenic glycosides

2.3 Medicinal plant omics

2.3.1 Genomics in medicinal plants

2.3.2 Transcriptomics in medicinal plant

2.3.3 Proteomics in medicinal plant

2.3.4 Metabolomics in medicinal plant

2.4 Future outlook

2.5 Conclusion

Further reading

3 - Multiomics approaches in cancer

3.1 Introduction

3.1.1 Multiomics approaches in cancer research

3.2 Epidemiology

3.3 Pathophysiology

3.3.1 Premalignant to malignant stages

3.4 Models to study cancer

3.4.1 Cell lines

3.4.2 Organoids

3.4.3 Mouse models

3.4.3.1 Transgenic mouse model of cancer

3.4.3.2 Gene targeting mouse model of cancer

3.4.3.3 Conditional and inducible mouse models for cancer

3.4.3.4 RNA interference mouse model of cancer.

3.4.3.5 Chromosomal engineering mouse model for cancer

3.4.3.6 Patient-derived xenografts

3.5 Multiomics for biomarker discovery for early diagnosis

3.5.1 Genomics

3.5.2 Transcriptomics

3.5.3 Proteomics

3.5.4 Metabolomics

3.6 Application of omics approaches to study cancer

3.6.1 Improving the therapeutic opportunities by new discoveries and alterations in genome for annotation of functions

3.6.2 Uncovering in an organization of interactions across various layers

3.6.2.1 Proteomics and transcriptomics

3.6.2.2 Epigenomics and transcriptomics

3.6.2.3 Metabolomics and transcriptomics

3.6.2.4 Extending profiling of molecular tumor

3.6.2.5 Assisting diagnosis of cancer on early basis

3.7 Novelity

3.8 Future prospects and challenges

3.9 Conclusion

Further reading (Books)

4 - Multiomics in cardiovascular diseases

4.1 Introduction

4.2 Cardiovascular diseases

4.2.1 Coronary heart disease

4.2.2 Angina

4.2.3 Stroke

4.2.4 Rheumatic heart disease

4.2.5 Congenital heart disease

4.2.6 Peripheral artery disease

4.2.7 Aortic aneurysm dissection

4.2.8 Deep vein thrombosis

4.2.9 Other cardiovascular diseases

4.3 Risk factors for cardiovascular diseases

4.3.1 Chain smoking

4.3.2 Obesity

4.3.3 Diabetes

4.3.4 Blood pressure

4.3.5 LDL cholesterol

4.3.6 Socioeconomic risks

4.3.7 Other related risk factors

4.4 Clinical spectrum

4.5 Omics approaches for mitigation of CVD

4.6 Omic data resources

4.6.1 The CVRG (Cardiovascular Research Grid)

4.6.2 The National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMeD) program

4.6.3 The Human Protein Atlas

4.6.4 The Genotype-Tissue Expression (GTEx) project

4.6.5 The European Bioinformatics Institute (EBI).

4.7 Applications of omics data

4.8 Challenges and limitations

4.9 Future directions

4.10 Conclusion

5 - Multiomics for understanding neurodegenerative disorders

5.1 Introduction to neurodegenerative disorders

5.2 Epidemiology

5.2.1 Alzheimer's disease

5.2.2 Parkinson's disease

5.2.3 Huntington's disease

5.2.4 Amyotrophic lateral sclerosis

5.3 Pathophysiology of neurodegenerative diseases

5.4 Genetics of neurodegenerative diseases

5.4.1 Alzheimer disease

5.4.2 Parkinson disease

5.4.3 Huntington disease

5.4.4 Amyotrophic lateral sclerosis

5.5 Genome-wide association studies

5.5.1 Alzheimer's and Parkinson's disease

5.5.2 GWAS in rare neurodegenerative diseases

5.6 Role of multiomic approaches in neurodegenerative disorders

5.6.1 Genomics

5.6.2 Transcriptomics

5.6.3 Proteomics

5.6.4 Lipidomics and metabolomics

5.7 Network-based multiomics integration approaches

5.8 Subtyping and integration based on machine learning

5.8.1 Integrative analysis for individual marker subtyping

5.8.2 Integrative analysis for composite markers

5.8.3 Outlier and non-Gaussian markers-based subtyping

5.8.4 Dynamic network biomarkers-based subtyping

5.8.5 Bioassay development for existing therapies

5.8.6 Validation and reliability

5.9 Biomarkers and their analysis

5.10 Conclusion

6 - Multiomics in gastrointestinal disorders

6.1 Introduction

6.2 Multiomics for investigating gastrointestinal disorders

6.2.1 Inflammatory bowel disease

6.2.1.1 Genomics

6.2.1.2 Epigenomics

6.2.1.3 Transcriptomics

6.2.1.4 Proteomics

6.2.2 Irritable bowel syndrome

6.2.2.1 Genomics/epigenomics

6.2.2.2 Transcriptomics

6.2.2.3 Proteomics

6.2.3 Colorectal cancer.

6.2.3.1 Genomics

6.2.3.2 Epigenomics

6.2.3.3 Transcriptomics

6.2.3.4 Proteomics

6.2.4 Celiac disease

6.2.5 Gastroesophageal reflux disorder

6.2.6 Peptic ulcer

6.3 Gastrointestinal stability resulting from gut disorders

6.3.1 Gut microbiomes

6.4 Integration methods and tools

6.4.1 Tools and methods for integrating multiomics data

6.4.1.1 Disease subtyping

6.4.1.2 Prediction of biomarkers

6.4.1.3 Deriving insights into disease biology

6.5 Tools/methods (for multiomics data analysis)

6.5.1 Bayesian multiomics approaches

6.5.2 iCluster and iClusterPlus

6.5.3 LRAcluster

6.5.4 Patient-specific data fusion (PSDF)

6.5.5 Bayesian consensus clustering (BCC)

6.5.6 Multiple dataset integration (MDI)

6.6 Conclusion

6.7 Current and future directions

Further reading (books)

7 - Multiomics in human viral infections

7.1 Introduction to viral diseases

7.2 Viral diseases role in multiomics

7.2.1 Genomics

7.2.2 Transcriptomics

7.2.3 Proteomics

7.2.4 Metabolomics

7.2.5 Significance of combination of various omics techniques that identifies potential biomarkers of the host in viral infections

7.3 Viral hijacking of cellular metabolism

7.3.1 Reprogramming energy metabolism

7.3.2 Nutrient availability manipulation

7.3.3 Lipid metabolism alteration

7.3.4 Subversion of signaling pathways

7.3.5 Modulation of host metabolic enzymes

7.3.6 Immune evasion by metabolic reprogramming

7.4 Distinct viral hijacking strategies in cellular metabolism

7.4.1 Human immunodeficiency virus

7.4.2 Herpesviruses

7.4.3 HCV (hepatitis C virus)

7.4.4 The influenza virus

7.4.5 HPV (human papillomavirus)

7.4.6 Retroviruses

7.4.7 Adenoviruses

7.4.8 Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).

7.5 Viruses as cancer discovery tools and therapies

7.5.1 Cancer discovery tools

7.5.2 Oncolytic viral therapies

7.5.3 Immunotherapy

7.5.4 Virus vectors for gene therapy

7.5.5 Clinical applications

7.6 Limitations and future prospects

7.6.1 Multiomics' limitations in viral diseases

7.6.1.1 Sample size and availability

7.6.1.2 Temporal dynamics

7.6.1.3 Functional annotation and interpretation

7.6.1.4 Technical variability

7.6.2 Future prospects of multiomics in viral diseases

7.6.2.1 Personalized medicine

7.6.2.2 Predictive models and early detection

7.6.2.3 Drug development and repurposing

7.6.2.4 Vaccine development

7.6.2.5 Systems-level insights

7.7 Panoramic advances of multiomics in viral infections

7.7.1 Comprehensive viral genome profiling

7.7.2 Unraveling host-virus interactions

7.7.3 Host immune response analysis

7.7.4 Viral and host biomarker discovery

7.7.5 Insights into metabolic reprogramming

7.7.6 Systems biology approaches

7.7.7 Personalized medicine strategies

7.7.8 Therapeutic development and repurposing

7.7.9 Vaccine development and evaluation

7.7.10 Big data and artificial intelligence

7.8 Conclusion

8 - Multiomics in autoimmune diseases

8.1 Introduction

8.2 Genetics

8.3 Heritability

8.3.1 Genetic loci for disease susceptibility

8.3.2 Major histocompatibility complex

8.3.2.1 Multiple associative indications

8.3.2.2 Classical HLA determinants

8.3.2.3 Epistasis and HLA

8.3.2.4 Disease-specific associations

8.3.3 Non-HLA susceptible loci

8.4 Contemporary diagnostic practices and disease management approaches

8.4.1 Costimulatory blockade

8.4.2 Regulatory T cell therapy

8.4.3 Functional Tregs isolation and expansion

8.4.4 Treg functionality.

8.4.5 Acute graft-versus-host disease.

Notes:

Description based on publisher supplied metadata and other sources.

Part of the metadata in this record was created by AI, based on the text of the resource.

ISBN:

9780443239700

0443239703

OCLC:

1436830267